Spanish National Research Council

About: Spanish National Research Council is a government organization based out in Madrid, Spain. It is known for research contribution in the topics: Population & Galaxy. The organization has 79563 authors who have published 220470 publications receiving 7698991 citations. The organization is also known as: CSIC & Consejo Superior de Investigaciones Científicas.

Hydrogen Peroxide Synthesis: An Outlook beyond the Anthraquinone Process

Abstract: Hydrogen peroxide (H2O2) is widely used in almost all industrial areas, particularly in the chemical industry and environmental protection. The only degradation product of its use is water, and thus it has played a large role in environmentally friendly methods in the chemical industry. Hydrogen peroxide is produced on an industrial scale by the anthraquinone oxidation (AO) process. However, this process can hardly be considered a green method. It involves the sequential hydrogenation and oxidation of an alkylanthraquinone precursor dissolved in a mixture of organic solvents followed by liquid–liquid extraction to recover H2O2. The AO process is a multistep method that requires significant energy input and generates waste, which has a negative effect on its sustainability and production costs. The transport, storage, and handling of bulk H2O2 involve hazards and escalating expenses. Thus, novel, cleaner methods for the production of H2O2 are being explored. The direct synthesis of H2O2 from O2 and H2 using a variety of catalysts, and the factors influencing the formation and decomposition of H2O2 are examined in detail in this Review.

Target mimicry provides a new mechanism for regulation of microRNA activity

Abstract: MicroRNAs (miRNA) regulate key aspects of development and physiology in animals and plants. These regulatory RNAs act as guides of effector complexes to recognize specific mRNA sequences based on sequence complementarity, resulting in translational repression or site-specific cleavage. In plants, most miRNA targets are cleaved and show almost perfect complementarity with the miRNAs around the cleavage site. Here, we examined the non-protein coding gene IPS1 (INDUCED BY PHOSPHATE STARVATION 1) from Arabidopsis thaliana. IPS1 contains a motif with sequence complementarity to the phosphate (Pi) starvation-induced miRNA miR-399, but the pairing is interrupted by a mismatched loop at the expected miRNA cleavage site. We show that IPS1 RNA is not cleaved but instead sequesters miR-399. Thus, IPS1 overexpression results in increased accumulation of the miR-399 target PHO2 mRNA and, concomitantly, in reduced shoot Pi content. Engineering of IPS1 to be cleavable abolishes its inhibitory activity on miR-399. We coin the term 'target mimicry' to define this mechanism of inhibition of miRNA activity. Target mimicry can be generalized beyond the control of Pi homeostasis, as demonstrated using artificial target mimics.

Bayesian Phylogenetic Analysis of Combined Data

Abstract: The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.